OpenRaiser/Intern-Atlas huggingface dataset:
Way to Specialist: Closing Loop Between Specialized LLM and Evolving Domain Knowledge Graph
Venue: arXiv cs.CL 2024
Year: 2024

Large language models (LLMs) have demonstrated exceptional performance across a wide variety of domains. Nonetheless, generalist LLMs continue to fall short in reasoning tasks necessitating specialized knowledge. Prior investigations into specialized LLMs focused on domain-specific training, which entails substantial efforts in domain data acquisition and model parameter fine-tuning. To address these challenges, this paper proposes the Way-to-Specialist (WTS) framework, which synergizes retrieval-augmented generation with knowledge graphs (KGs) to enhance the specialized capability of LLMs in the absence of specialized training. In distinction to existing paradigms that merely utilize external knowledge from general KGs or static domain KGs to prompt LLM for enhanced domain-specific reasoning, WTS proposes an innovative "LLM$\circlearrowright$KG" paradigm, which achieves bidirectional enhancement between specialized LLM and domain knowledge graph (DKG). The proposed paradigm encompasses two closely coupled components: the DKG-Augmented LLM and the LLM-Assisted DKG Evolution. The former retrieves question-relevant domain knowledge from DKG and uses it to prompt LLM to enhance the reasoning capability for domain-specific tasks; the latter leverages LLM to generate new domain knowledge from processed tasks and use it to evolve DKG. WTS closes the loop between DKG-Augmented LLM and LLM-Assisted DKG Evolution, enabling continuous improvement in the domain specialization as it progressively answers and learns from domain-specific questions. We validate the performance of WTS on 6 datasets spanning 5 domains. The experimental results show that WTS surpasses the previous SOTA in 4 specialized domains and achieves a maximum performance improvement of 11.3%.
VARCO-VISION: Expanding Frontiers in Korean Vision-Language Models
Venue: arXiv cs.CV 2024
Year: 2024

In this paper, we introduce an open-source Korean-English vision-language model (VLM), VARCO-VISION. We incorporate a step-by-step training strategy that allows a model learn both linguistic and visual information while preserving the backbone model's knowledge. Our model demonstrates outstanding performance in diverse settings requiring bilingual image-text understanding and generation abilities compared to models of similar size. VARCO-VISION is also capable of grounding, referring, and OCR, expanding its usage and potential applications for real-world scenarios. In addition to the model, we release five Korean evaluation datasets, including four closed-set and one openset benchmarks. We anticipate that our milestone will broaden the opportunities for AI researchers aiming to train VLMs. VARCO-VISION is available at https://huggingface.co/NCSOFT/VARCO-VISION-14B.
A Comparative Analysis of Vulnerability Management Tools: Evaluating Nessus, Acunetix, and Nikto for Risk Based Security Solutions
Venue: arXiv cs.CR 2024
Year: 2024

The evolving threat landscape in cybersecurity necessitates the adoption of advanced tools for effective vulnerability management. This paper presents a comprehensive comparative analysis of three widely used tools: Nessus, Acunetix, and Nikto. Each tool is assessed based on its detection accuracy, risk scoring using the Common Vulnerability Scoring System (CVSS), ease of use, automation and reporting capabilities, performance metrics, and cost effectiveness. The research addresses the challenges faced by organizations in selecting the most suitable tool for their unique security requirements.
DESIRE: Dynamic Knowledge Consolidation for Rehearsal-Free Continual Learning
Venue: arXiv cs.LG 2024
Year: 2024

Continual learning aims to equip models with the ability to retain previously learned knowledge like a human. Recent work incorporating Parameter-Efficient Fine-Tuning has revitalized the field by introducing lightweight extension modules. However, existing methods usually overlook the issue of information leakage caused by the fact that the experiment data have been used in pre-trained models. Once these duplicate data are removed in the pre-training phase, their performance can be severely affected. In this paper, we propose a new LoRA-based rehearsal-free method named DESIRE. Our method avoids imposing additional constraints during training to mitigate catastrophic forgetting, thereby maximizing the learning of new classes. To integrate knowledge from old and new tasks, we propose two efficient post-processing modules. On the one hand, we retain only two sets of LoRA parameters for merging and propose dynamic representation consolidation to calibrate the merged feature representation. On the other hand, we propose decision boundary refinement to address classifier bias when training solely on new class data. Extensive experiments demonstrate that our method achieves state-of-the-art performance on multiple datasets and strikes an effective balance between stability and plasticity. Our code will be publicly available.
LoRA of Change: Learning to Generate LoRA for the Editing Instruction from A Single Before-After Image Pair
Venue: arXiv cs.CV 2024
Year: 2024

In this paper, we propose the LoRA of Change (LoC) framework for image editing with visual instructions, i.e., before-after image pairs. Compared to the ambiguities, insufficient specificity, and diverse interpretations of natural language, visual instructions can accurately reflect users' intent. Building on the success of LoRA in text-based image editing and generation, we dynamically learn an instruction-specific LoRA to encode the "change" in a before-after image pair, enhancing the interpretability and reusability of our model. Furthermore, generalizable models for image editing with visual instructions typically require quad data, i.e., a before-after image pair, along with query and target images. Due to the scarcity of such quad data, existing models are limited to a narrow range of visual instructions. To overcome this limitation, we introduce the LoRA Reverse optimization technique, enabling large-scale training with paired data alone. Extensive qualitative and quantitative experiments demonstrate that our model produces high-quality images that align with user intent and support a broad spectrum of real-world visual instructions.
Z-STAR+: A Zero-shot Style Transfer Method via Adjusting Style Distribution
Venue: arXiv cs.CV 2024
Year: 2024

Style transfer presents a significant challenge, primarily centered on identifying an appropriate style representation. Conventional methods employ style loss, derived from second-order statistics or contrastive learning, to constrain style representation in the stylized result. However, these pre-defined style representations often limit stylistic expression, leading to artifacts. In contrast to existing approaches, we have discovered that latent features in vanilla diffusion models inherently contain natural style and content distributions. This allows for direct extraction of style information and seamless integration of generative priors into the content image without necessitating retraining. Our method adopts dual denoising paths to represent content and style references in latent space, subsequently guiding the content image denoising process with style latent codes. We introduce a Cross-attention Reweighting module that utilizes local content features to query style image information best suited to the input patch, thereby aligning the style distribution of the stylized results with that of the style image. Furthermore, we design a scaled adaptive instance normalization to mitigate inconsistencies in color distribution between style and stylized images on a global scale. Through theoretical analysis and extensive experimentation, we demonstrate the effectiveness and superiority of our diffusion-based \uline{z}ero-shot \uline{s}tyle \uline{t}ransfer via \uline{a}djusting style dist\uline{r}ibution, termed Z-STAR+.
Perspective of Software Engineering Researchers on Machine Learning Practices Regarding Research, Review, and Education
Venue: arXiv cs.SE 2024
Year: 2024

Context: Machine Learning (ML) significantly impacts Software Engineering (SE), but studies mainly focus on practitioners, neglecting researchers. This overlooks practices and challenges in teaching, researching, or reviewing ML applications in SE. Objective: This study aims to contribute to the knowledge, about the synergy between ML and SE from the perspective of SE researchers, by providing insights into the practices followed when researching, teaching, and reviewing SE studies that apply ML. Method: We analyzed SE researchers familiar with ML or who authored SE articles using ML, along with the articles themselves. We examined practices, SE tasks addressed with ML, challenges faced, and reviewers' and educators' perspectives using grounded theory coding and qualitative analysis. Results: We found diverse practices focusing on data collection, model training, and evaluation. Some recommended practices (e.g., hyperparameter tuning) appeared in less than 20\% of literature. Common challenges involve data handling, model evaluation (incl. non-functional properties), and involving human expertise in evaluation. Hands-on activities are common in education, though traditional methods persist. Conclusion: Despite accepted practices in applying ML to SE, significant gaps remain. By enhancing guidelines, adopting diverse teaching methods, and emphasizing underrepresented practices, the SE community can bridge these gaps and advance the field.
An Adversarial Learning Approach to Irregular Time-Series Forecasting
Venue: arXiv cs.LG 2024
Year: 2024

Forecasting irregular time series presents significant challenges due to two key issues: the vulnerability of models to mean regression, driven by the noisy and complex nature of the data, and the limitations of traditional error-based evaluation metrics, which fail to capture meaningful patterns and penalize unrealistic forecasts. These problems result in forecasts that often misalign with human intuition. To tackle these challenges, we propose an adversarial learning framework with a deep analysis of adversarial components. Specifically, we emphasize the importance of balancing the modeling of global distribution (overall patterns) and transition dynamics (localized temporal changes) to better capture the nuances of irregular time series. Overall, this research provides practical insights for improving models and evaluation metrics, and pioneers the application of adversarial learning in the domian of irregular time-series forecasting.
Dynamic Taint Tracking using Partial Instrumentation for Java Applications
Venue: arXiv cs.CR 2024
Year: 2024

Dynamic taint tracking is the process of assigning label to variables in a program and then tracking the flow of the labels as the program executes. Dynamic taint tracking for java applications is achieved by instrumenting the application ie. adding parallel variable for each actual variable of the program and inserting additional bytecode instructions to track the flow of the parallel variables. In this paper we suggest partial instrumentation to achieve dynamic taint tracking with reasonable runtime overhead. Partial instrumentation involves instrumenting only parts of a java application, which are within the scope of a predefined source and sink set. Partial instrumentation is performed at the granularity level of a method. We use PetaBlox, a large-scale software analysis tool, which internally uses Datalog[3], to perform static analysis and infers all the methods within the scope of source and sink sets and a modified version of Phosphor[1] to achieve partial instrumentation. Test runs performed on some of the Dacapo benchmarks show a significant performance improvement over the version of Phosphor that performs complete instrumentation.
Characterizing JavaScript Security Code Smells
Venue: arXiv cs.CR 2024
Year: 2024

JavaScript has been consistently among the most popular programming languages in the past decade. However, its dynamic, weakly-typed, and asynchronous nature can make it challenging to write maintainable code for developers without in-depth knowledge of the language. Consequently, many JavaScript applications tend to contain code smells that adversely influence program comprehension, maintenance, and debugging. Due to the widespread usage of JavaScript, code security is an important matter. While JavaScript code smells and detection techniques have been studied in the past, current work on security smells for JavaScript is scarce. Security code smells are coding patterns indicative of potential vulnerabilities or security weaknesses. Identifying security code smells can help developers to focus on areas where additional security measures may be needed. We present a set of 24 JavaScript security code smells, map them to a possible security awareness defined by Common Weakness Enumeration (CWE), explain possible refactoring, and explain our detection mechanism. We implement our security code smell detection on top of an existing open source tool that was proposed to detect general code smells in JavaScript.
DreamBlend: Advancing Personalized Fine-tuning of Text-to-Image Diffusion Models
Venue: arXiv cs.CV 2024
Year: 2024

Given a small number of images of a subject, personalized image generation techniques can fine-tune large pre-trained text-to-image diffusion models to generate images of the subject in novel contexts, conditioned on text prompts. In doing so, a trade-off is made between prompt fidelity, subject fidelity and diversity. As the pre-trained model is fine-tuned, earlier checkpoints synthesize images with low subject fidelity but high prompt fidelity and diversity. In contrast, later checkpoints generate images with low prompt fidelity and diversity but high subject fidelity. This inherent trade-off limits the prompt fidelity, subject fidelity and diversity of generated images. In this work, we propose DreamBlend to combine the prompt fidelity from earlier checkpoints and the subject fidelity from later checkpoints during inference. We perform a cross attention guided image synthesis from a later checkpoint, guided by an image generated by an earlier checkpoint, for the same prompt. This enables generation of images with better subject fidelity, prompt fidelity and diversity on challenging prompts, outperforming state-of-the-art fine-tuning methods.
Hashing for Sampling-Based Estimation
Venue: arXiv cs.DS 2024
Year: 2024

Hash-based sampling and estimation are common themes in computing. Using hashing for sampling gives us the coordination needed to compare samples from different sets. Hashing is also used when we want to count distinct elements. The quality of the estimator for, say, the Jaccard similarity between two sets, depends on the concentration of the number of sampled elements from their intersection. Often we want to compare one query set against many stored sets to find one of the most similar sets, so we need strong concentration and low error-probability. In this paper, we provide strong explicit concentration bounds for Tornado Tabulation hashing [Bercea, Beretta, Klausen, Houen, and Thorup, FOCS'23] which is a realistic constant time hashing scheme. Previous concentration bounds for fast hashing were off by orders of magnitude, in the sample size needed to guarantee the same concentration. The true power of our result appears when applied in the local uniformity framework by [Dahlgaard, Knudsen, Rotenberg, and Thorup, STOC'15].
WDD: Weighted Delta Debugging
Venue: arXiv cs.SE 2024
Year: 2024

Delta Debugging is a widely used family of algorithms (e.g., ddmin and ProbDD) to automatically minimize bug-triggering test inputs, thus to facilitate debugging. It takes a list of elements with each element representing a fragment of the test input, systematically partitions the list at different granularities, identifies and deletes bug-irrelevant partitions. Prior delta debugging algorithms assume there are no differences among the elements in the list, and thus treat them uniformly during partitioning. However, in practice, this assumption usually does not hold, because the size (referred to as weight) of the fragment represented by each element can vary significantly. For example, a single element representing 50% of the test input is much more likely to be bug-relevant than elements representing only 1%. This assumption inevitably impairs the efficiency or even effectiveness of these delta debugging algorithms. This paper proposes Weighted Delta Debugging (WDD), a novel concept to help prior delta debugging algorithms overcome the limitation mentioned above. The key insight of WDD is to assign each element in the list a weight according to its size, and distinguish different elements based on their weights during partitioning. We designed two new minimization algorithms, Wddmin and WProbDD, by applying WDD to ddmin and ProbDD respectively. We extensively evaluated Wddmin and WProbDD in two representative applications, HDD and Perses, on 62 benchmarks across two languages. The results strongly demonstrate the value of WDD. We firmly believe that WDD opens up a new dimension to improve test input minimization techniques.
Auto-RAG: Autonomous Retrieval-Augmented Generation for Large Language Models
Venue: arXiv cs.CL 2024
Year: 2024

Iterative retrieval refers to the process in which the model continuously queries the retriever during generation to enhance the relevance of the retrieved knowledge, thereby improving the performance of Retrieval-Augmented Generation (RAG). Existing work typically employs few-shot prompting or manually constructed rules to implement iterative retrieval. This introduces additional inference overhead and overlooks the remarkable reasoning capabilities of Large Language Models (LLMs). In this paper, we introduce Auto-RAG, an autonomous iterative retrieval model centered on the LLM's powerful decision-making capabilities. Auto-RAG engages in multi-turn dialogues with the retriever, systematically planning retrievals and refining queries to acquire valuable knowledge. This process continues until sufficient external information is gathered, at which point the results are presented to the user. To this end, we develop a method for autonomously synthesizing reasoning-based decision-making instructions in iterative retrieval and fine-tuned the latest open-source LLMs. The experimental results indicate that Auto-RAG is capable of autonomous iterative interaction with the retriever, effectively leveraging the remarkable reasoning and decision-making abilities of LLMs, which lead to outstanding performance across six benchmarks. Further analysis reveals that Auto-RAG can autonomously adjust the number of iterations based on the difficulty of the questions and the utility of the retrieved knowledge, without requiring any human intervention. Moreover, Auto-RAG expresses the iterative retrieval process in natural language, enhancing interpretability while providing users with a more intuitive experience\footnote{Code is available at \url{https://github.com/ictnlp/Auto-RAG}.
Look Every Frame All at Once: Video-Ma$^2$mba for Efficient Long-form Video Understanding with Multi-Axis Gradient Checkpointing
Venue: arXiv cs.CV 2024
Year: 2024

With the growing scale and complexity of video data, efficiently processing long video sequences poses significant challenges due to the quadratic increase in memory and computational demands associated with existing transformer-based Large Multi-modal Models (LMMs). To address these issues, we introduce Video-Ma$^2$mba, a novel architecture that incorporates State Space Models (SSMs) within the Mamba-2 framework, replacing the attention mechanisms. This allows the LMMs to scale linearly in terms of time and memory requirements, making it feasible to handle long-duration video content. Furthermore, we enhance the memory efficiency introducing the Multi-Axis Gradient Checkpointing (MA-GC) method, which strategically manages memory by retaining only essential activations across multiple computational axes. Our approach significantly reduces the memory footprint compared to standard gradient checkpointing. Empirical analyses show that Video-Ma$^2$mba can process extensive video sequences-equivalent to millions of tokens or over two hours of continuous sequences at 1 FPS-on a single GPU. By maintaining a detailed capture of temporal dynamics, our model improves the accuracy and relevance of responses in long video understanding tasks, demonstrating substantial advantages over existing frameworks.
Protecting Multiple Types of Privacy Simultaneously in EEG-based Brain-Computer Interfaces
Venue: arXiv cs.HC 2024
Year: 2024

A brain-computer interface (BCI) enables direct communication between the brain and an external device. Electroencephalogram (EEG) is the preferred input signal in non-invasive BCIs, due to its convenience and low cost. EEG-based BCIs have been successfully used in many applications, such as neurological rehabilitation, text input, games, and so on. However, EEG signals inherently carry rich personal information, necessitating privacy protection. This paper demonstrates that multiple types of private information (user identity, gender, and BCI-experience) can be easily inferred from EEG data, imposing a serious privacy threat to BCIs. To address this issue, we design perturbations to convert the original EEG data into privacy-protected EEG data, which conceal the private information while maintaining the primary BCI task performance. Experimental results demonstrated that the privacy-protected EEG data can significantly reduce the classification accuracy of user identity, gender and BCI-experience, but almost do not affect at all the classification accuracy of the primary BCI task, enabling user privacy protection in EEG-based BCIs.
ContextGNN: Beyond Two-Tower Recommendation Systems
Venue: arXiv cs.IR 2024
Year: 2024

Recommendation systems predominantly utilize two-tower architectures, which evaluate user-item rankings through the inner product of their respective embeddings. However, one key limitation of two-tower models is that they learn a pair-agnostic representation of users and items. In contrast, pair-wise representations either scale poorly due to their quadratic complexity or are too restrictive on the candidate pairs to rank. To address these issues, we introduce Context-based Graph Neural Networks (ContextGNNs), a novel deep learning architecture for link prediction in recommendation systems. The method employs a pair-wise representation technique for familiar items situated within a user's local subgraph, while leveraging two-tower representations to facilitate the recommendation of exploratory items. A final network then predicts how to fuse both pair-wise and two-tower recommendations into a single ranking of items. We demonstrate that ContextGNN is able to adapt to different data characteristics and outperforms existing methods, both traditional and GNN-based, on a diverse set of practical recommendation tasks, improving performance by 20% on average.